Servicenow, Inc.

Atul Saini

Sharath Vaddempudi

Magesh Narayanan

Kavish Jain

Yerjan Khurmyetbyek

Swathi Pattapu

Siddharth Rajendra Shah

Joseph Michael Davis

Varsha Herle

Xiaoyi Ye

Shaoying Zou

Yang Xiang

Wenhui Li

Shahil Sabbag

Scott Michael Nemes

Venu Vajjala

Yasaswini Potta

Xuri Yu

Sethuraman Meiyappan

Cody Stephen John

Manjunath Gurubasappa Vagadurgi

Various approaches are described to manage the execution of operations. Such operations may be performed without human intervention and may help maintain functionality of a cloud platform or client instances. In one aspect of the present approach, the number and/or type of automations starting in a given time frame may be limited to maintain an even or consistent distribution of resource usage. In a further aspect, the number and/or type of concurrent automations may be limited to a defined threshold to maintain an even or consistent distribution of resource usage.

Resource and operation conflict identification on a cloud platform

A method includes generating, for display by way of a graphical user interface (GUI), a graphical representation of an existing state of a plurality of computing resources that visually represents the plurality of computing resources and one or more relationships therebetween. The method also includes determining a target state of the plurality of computing resources based on a user modification of the graphical representation of the existing state. The user modification may be obtained by way of the GUI. The method additionally includes determining, based on a difference between the target state and the existing state, one or more operations configured to modify the plurality of computing resources to reach the target state from the existing state. The method further includes executing the one or more operations.

Outcome-oriented automation platform

Systems and methods are disclosed that acquire server resources at the time of scheduling an automated instance-related task, such as an instance migration task, and prior to starting the automated task (e.g., prior to determining scheduling conflicts, creating a change request, or creating a move context associated with starting the instance migration task). Advantageously, if acquiring the server resources fails, an orchestration server performing the automated task can simply retry acquiring the server resources, thus avoiding restarting the automated task and re-performing steps of the automated task, thus avoiding unnecessary overhead.

Systems and methods for acquiring server resources at schedule time

Mitigating resource scheduling conflicts in a cloud platform

Input information comprising an identifier of a production instance is received. The input information indicating a given time a live database from the production instance is to be restored to in a temporary copy of the production instance. Based at least on the input information, backup data associated with the live database from production instance is identified. Based on the backup data, it is determined whether resource capacity is available for restoring the backup data to the temporary copy of the production instance. In response to determining the resource capacity is available, the temporary copy of the production instance is generated. The backup data is restored to the temporary copy of the production instance to represent a state of the live database from the production instance at the given time.

Auto point in time data restore for instance copy

Systems, apparatuses, and methods for cloud memory sizing are disclosed. An initial database memory allocation is determined for the provisioning of a database server instance. Periodically, sizes of key database tables of the database server instance are measured and an upper and a lower bound ratio are determined based on the key database table sizes and a buffer pool size. The upper and lower bound ratios are used to determine a desired memory allocation from which a report is generated including an interface for generating an instance move action for re-provisioning the database server instance with the desired memory allocation.

Techniques and devices for cloud memory sizing

Growing community of inventors

Bellevue, WA, United States of America

Atul Saini